# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

"""Dataset for filtered Kvasir-instrument and Hyper-Kvasir with bounding boxes."""

import os
import json
from PIL import Image
import datasets

import os
import json
import pandas as pd
import hashlib


cal_mid = lambda bx: [[[float(box['xmin'] + box['xmax']) / 2, float(box['ymin'] + box['ymax']) / 2] for box in bx]]


def cal_sha256(file_path): return hashlib.sha256(
    open(file_path, 'rb').read()).hexdigest()


hyper_label_img_path = '/global/D1/projects/HOST/Datasets/hyper-kvasir/labeled-images/image-labels.csv'

hyper_df = pd.read_csv(hyper_label_img_path)

hyper_seg_img_path = '/global/D1/projects/HOST/Datasets/hyper-kvasir/segmented-images/bounding-boxes.json'
hyper_seg_img_base_path = "/global/D1/projects/HOST/Datasets/hyper-kvasir/segmented-images/images"

instr_seg_img_path = '/global/D1/projects/HOST/Datasets/kvasir-instrument/bboxes.json'
instr_seg_img_base_path = '/global/D1/projects/HOST/Datasets/kvasir-instrument/images/'

hyper_seg_imgs = json.load(open(hyper_seg_img_path))
instr_seg_imgs = json.load(open(instr_seg_img_path))

_CITATION = """\
@article{kvasir,
    title={Kvasir-instrument and Hyper-Kvasir datasets for bounding box annotations},
    author={Sushant Gautam and collaborators},
    year={2024}
}
"""

_DESCRIPTION = """
Filtered Kvasir-instrument and Hyper-Kvasir datasets with bounding boxes for medical imaging tasks. 
Each entry contains images, bounding box coordinates, and additional metadata.
"""

_HOMEPAGE = "https://example.com/kvasir-hyper-bbox"

_LICENSE = "CC BY-NC 4.0"

_URLS = {
    "filtered_data": "https://example.com/kvasir-hyper-bbox-dataset.zip"
}


class KvasirHyperBBox(datasets.GeneratorBasedBuilder):
    """Dataset for Kvasir-instrument and Hyper-Kvasir with bounding boxes."""

    VERSION = datasets.Version("1.0.0")

    BUILDER_CONFIGS = [
        datasets.BuilderConfig(
            name="bbox_dataset",
            version=VERSION,
            description="Dataset with bounding box annotations."
        )
    ]

    DEFAULT_CONFIG_NAME = "bbox_dataset"

    def _info(self):
        features = datasets.Features({
            "image_data": datasets.Image(),
            "image_sha256": datasets.Value("string"),
            "points": datasets.Sequence(datasets.Sequence(datasets.Sequence(datasets.Value("float32")))),
            "count": datasets.Value("int64"),
            "label": datasets.Value("string"),
            "collection_method": datasets.Value("string"),
            "classification": datasets.Value("string"),
            "organ": datasets.Value("string")
        })

        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            homepage=_HOMEPAGE,
            license=_LICENSE,
            citation=_CITATION,
            features=features
        )

    def _split_generators(self, dl_manager):
        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                gen_kwargs={},
            )
        ]

    def _generate_examples(self):
        for key, entry in hyper_seg_imgs.items():
            img_path = os.path.join(hyper_seg_img_base_path, f"{key}.jpg")
            hyper_entry = hyper_df.loc[hyper_df['Video file'] == key].iloc[0]
            yield key, {
                "image_data": open(img_path, 'rb').read(),
                "image_sha256": cal_sha256(img_path),
                "points":  cal_mid(entry['bbox']),
                "count": len(entry['bbox']),
                "label": hyper_entry.Finding,
                "collection_method": 'counting',
                "classification": hyper_entry.Classification,
                "organ": hyper_entry.Organ 
            }

        for key, entry in instr_seg_imgs.items():
            img_path = os.path.join(instr_seg_img_base_path, f"{key}.jpg")
            assert len(cal_mid(entry['bbox'])) > 0
            yield key, {
                "image_data": open(img_path, 'rb').read(),
                "image_sha256": cal_sha256(img_path),
                "points":  cal_mid(entry['bbox']),
                "count": len(entry['bbox']),
                "label": "instrument",
                "collection_method": "counting",
                "classification": "instrument",
                "organ": "instrument"
            }

#datasets-cli test  /global/D1/projects/HOST/Datasets/hyper-kvasir/sushant-experiments/kvasir-points_datasets_script.py --save_info --all_configs --trust_remote_code
# huggingface-cli upload kvasir-points . . --repo-type dataset